get_signal_scoresCloselook’s proprietary per-ticker scoring for ~300 covered names: composite rank, the seven sub-scores (quality, growth, valuation, balance, cash quality, capital allocation, momentum) under the sector-appropriate weight set, plus literature ratios (Altman Z, Piotroski F, Beneish M). End-of-day.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Ticker, e.g. DDOG |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context: the data is end-of-day, scoring uses sector-appropriate weights, and coverage is limited to ~300 names. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The first sentence efficiently packs the tool's output components; the second notes end-of-day timing. Every clause adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description does well to enumerate the returned components (composite rank, seven sub-scores, ratios). It could mention behavior for uncovered tickers or score interpretation, but the provided information is largely sufficient for an agent to understand the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the ticker parameter with a clear example (DDOG), and coverage is 100%. The description adds the context that the ticker must be among ~300 covered names, but it doesn't elaborate on parameter format or constraints beyond the schema's example. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides per-ticker scoring for ~300 covered names, listing the composite rank, seven sub-scores, and literature ratios. This specific verb+resource (get scores) distinguishes it from sibling tools like get_price_history or compare_indices.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context makes it clear this is for retrieving Closelook's proprietary scoring for a single ticker, but it does not explicitly state when to use it versus alternatives or mention limiting conditions like coverage scope. The covered-names limitation is implied, though not framed as a usage guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to a distinct Closelook surface — indices, macro boards, rankings, research, tape, wires. There are a few near-neighbor pairs like get_df_ranking vs get_ranking and get_market_instrument vs get_stock_snapshot, but the detailed descriptions resolve most potential misselection.
Every tool follows a clear imperative verb + noun pattern — get_, define_term, compare_indices, search_closelook — with consistent snake_case and essentially all resource-oriented nouns. The naming is predictable and easy to navigate.
25 tools is on the heavy side, above the typical well-scoped range, but each endpoint maps to a meaningful product area of Closelook's public site. The count is justified by the breadth of a financial intelligence service, though it could have been trimmed slightly.
The tool surface covers the major Closelook workflows: indices, rankings, macro monitors, stock-level data, earnings, wires, research search, and portfolio snapshots. For a read-only intelligence server there are no obvious dead ends — depth and background material is paired with lookups and daily updates.